Fragment-based and classical quantitative structure–activity relationships for a series of hydrazides as antituberculosis agents

4-(Dimethylamino)benzohydrazide

The title mol­ecule is essentially flat; in the crystal the mol­ecules are linked by a system of hydrogen bonds formed by the hydrazido group and consisting of chains of fused rings.

The title compound, C₉H₁₃N₃O, crystallizes in the monoclinic space group C2/c and all non-hydrogen atoms are within 0.1 Å of the mol­ecular mean plane. In the crystal, the hydrogen-bonding pattern results in [001] chains built up from fused R₂²(6) and R₂²(10) rings; the former consists of N—H⋯N bonds and the latter N—H⋯O bonds. Electrostatic and dispersion forces are major contributors to the lattice energy, which was estimated by DFT calculations to be −215.7 kJ mol⁻¹.

QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new heteroaryl chalcone compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9, 11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 μM and <10 μM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-TB agents.

In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni

Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes.

Schistosomiasis is a neglected tropical disease caused by schistosome parasites that affects millions of people worldwide. The current reliance on a single drug (Praziquantel) for treatment and control of the disease calls for the urgent discovery of novel schistosomicidal agents. One approach that can expedite drug discovery is to find new uses for existing approved drugs, a practice known as drug repositioning. Currently, modern drug repositioning strategies entail the search for compounds that act on a specific target, often a protein known or suspected to be required for survival of the parasite. Drug repositioning approaches for schistosomiasis are now greatly facilitated by the availability of comprehensive schistosome genome data in user-friendly databases. Here, we report a drug repositioning computational strategy that involves identification of novel schistosomicidal drug candidates using similarity between schistosome proteins and known drug targets. Researchers can now use the list of predicted drugs as a basis for deciding which potential schistosomicidal candidates can be tested experimentally.

Elucidating the structural basis of diphenyl ether derivatives as highly potent enoyl-ACP reductase inhibitors through molecular dynamics simulations and 3D-QSAR study

Diphenyl ether derivatives are good candidates for anti-tuberculosis agents that display a promising potency for inhibition of InhA, an essential enoyl-acyl carrier protein (ACP) reductase involved in fatty acid biosynthesis pathways in Mycobacterium tuberculosis. In this work, key structural features for the inhibition were identified by 3D-QSAR CoMSIA models, constructed based on available experimental binding properties of diphenyl ether inhibitors, and a set of four representative compounds was subjected to MD simulations of inhibitor-InhA complexes for the calculation of binding free energies. The results show that bulky groups are required for the R1 substituent on the phenyl A ring of the inhibitors to favor a hydrophobic pocket formed by residues Phe149, Met155, Pro156, Ala157, Tyr158, Pro193, Met199, Val203, Leu207, Ile215, and Leu218. Small substituents with a hydrophilic property are required at the R3 and R4 positions of the inhibitor phenyl B rings to form hydrogen bonds with the backbones of Gly96 and Met98, respectively. For the R2 substituent, small substituents with simultaneous hydrophilic or hydrophobic properties are required to favor the interaction with the pyrophosphate moiety of NAD(+) and the methyl side chain of Ala198, respectively. The reported data provide structural guidance for the design of new and potent diphenyl ether-based inhibitors with high inhibitory activities against M. tuberculosis InhA.

Computational models for tuberculosis drug discovery

The search for small molecules with activity against Mycobacterium tuberculosis increasingly uses -high-throughput screening and computational methods. Previously, we have analyzed recent studies in which computational tools were used for cheminformatics. We have now updated this analysis to illustrate how they may assist in finding desirable leads for tuberculosis drug discovery. We provide our thoughts on strategies for drug discovery efforts for neglected diseases.

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.

Recent advances in fragment-based QSAR and multi-dimensional QSAR methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

Elucidating drug-enzyme interactions and their structural basis for improving the affinity and potency of isoniazid and its derivatives based on computer modeling approaches

The enoyl-ACP reductase enzyme (InhA) from M. tuberculosis is recognized as the primary target of isoniazid (INH), a first-line antibiotic for tuberculosis treatment. To identify the specific interactions of INH-NAD adduct and its derivative adducts in InhA binding pocket, molecular docking calculations and quantum chemical calculations were performed on a set of INH derivative adducts. Reliable binding modes of INH derivative adducts in the InhA pocket were established using the Autodock 3.05 program, which shows a good ability to reproduce the X-ray bound conformation with rmsd of less than 1.0 A. The interaction energies of the INH-NAD adduct and its derivative adducts with individual amino acids in the InhA binding pocket were computed based on quantum chemical calculations at the MP2/6-31G (d) level. The molecular docking and quantum chemical calculation results reveal that hydrogen bond interactions are the main interactions for adduct binding. To clearly delineate the linear relationship between structure and activity of these adducts, CoMFA and CoMSIA models were set up based on molecular docking alignment. The resulting CoMFA and CoMSIA models are in conformity with the best statistical qualities, in which r2cv is 0.67 and 0.74, respectively. Structural requirements of isoniazid derivatives that can be incorporated into the isoniazid framework to improve the activity have been identified through CoMFA and CoMSIA steric and electrostatic contour maps. The integrated results from structure-based, ligand-based design approaches and quantum chemical calculations provide useful structural information facilitating the design of new and more potentially effective antitubercular agents as follow: the R substituents of isoniazid derivatives should contain a large plane and both sides of the plane should contain an electropositive group. Moreover, the steric and electrostatic fields of the 4-pyridyl ring are optimal for greater potency.

Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpha (hERalpha) and beta (hERbeta). Because the levels and relative proportion of hERalpha and hERbeta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hERalpha and hERbeta. Significant statistical coefficients were obtained (hERalpha, q(2) = 0.76; hERbeta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hERalpha and hERbeta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design of novel hER modulators with improved selectivity.

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.